Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!
This work addresses the problem of financial forecasting for traders and analysts, showing that traditional methods can still be superior, making it an incremental contribution.
The study compared deep-learning techniques, including data augmentation and meta-learning, against ARIMA for forecasting financial market prices, finding that ARIMA outperformed all deep-learning methods.
Deep-learning techniques have been successfully used for time-series forecasting and have often shown superior performance on many standard benchmark datasets as compared to traditional techniques. Here we present a comprehensive and comparative study of performance of deep-learning techniques for forecasting prices in financial markets. We benchmark state-of-the-art deep-learning baselines, such as NBeats, etc., on data from currency as well as stock markets. We also generate synthetic data using a fuzzy-logic based model of demand driven by technical rules such as moving averages, which are often used by traders. We benchmark the baseline techniques on this synthetic data as well as use it for data augmentation. We also apply gradient-based meta-learning to account for non-stationarity of financial time-series. Our extensive experiments notwithstanding, the surprising result is that the standard ARIMA models outperforms deep-learning even using data augmentation or meta-learning. We conclude by speculating as to why this might be the case.